Summary of Sketch-plan-generalize: Continual Few-shot Learning Of Inductively Generalizable Spatial Concepts, by Namasivayam Kalithasan et al.
Sketch-Plan-Generalize: Continual Few-Shot Learning of Inductively Generalizable Spatial Concepts
by Namasivayam Kalithasan, Sachit Sachdeva, Himanshu Gaurav Singh, Vishal Bindal, Arnav Tuli, Gurarmaan Singh Panjeta, Divyanshu Aggarwal, Rohan Paul, Parag Singla
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel learning architecture is proposed to enable embodied agents to learn spatial concepts through human demonstrations. The approach aims to infer a succinct program representation that explains the observed instance and generalizes inductively to novel structures. The key insight lies in factorizing concept learning into three stages: Sketch, Plan, and Generalize. The pipeline combines the benefits of large language models (LLM) and grounded neural representations, resulting in neuro-symbolic programs with stronger inductive generalization compared to LLM-only and neural-only approaches. Furthermore, the learned concepts enable reasoning and planning capabilities for embodied instruction following. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way for robots or machines to learn about shapes and spaces. They want these machines to be able to understand what we mean when we demonstrate something to them. For example, if you show a robot how to build a staircase using blocks, they want the robot to learn that the staircase is made up of towers stacked on top of each other. The researchers came up with a three-step process: first, the machine detects the basic idea behind what it’s seeing (like the shape of the blocks), then it uses this information to plan how to build something similar, and finally, it learns to apply these plans in different situations. This approach helps machines learn faster and more accurately than before, which is important for robots that need to follow instructions or learn from humans. |
Keywords
» Artificial intelligence » Generalization
